Since the early 2000s, educational evaluation research has primarily centered on teachers’, rather than schools’, contributions to students’ academic outcomes due to concerns that estimates of the latter were smaller, less stable, and more prone to measurement error. We argue that this disparity should be reduced. Using administrative data from three cohorts of Massachusetts public school students (N = 123,261) and two-level models, we estimate middle schools’ value-added effects on eighth-grade and 10th-grade math scores and, importantly, a non–test score outcome: 4-year college enrollment. Comparing our results to teacher-centered studies, we find that school effects (encompassing both teaching- and nonteaching-related factors) are initially smaller but nearly as stable and perhaps more persistent than are individual teacher effects. Our study motivates future research estimating the long-term effects of both teachers and schools on a wide range of outcomes.
AndermanE. M.MaehrM. L. (1994). Motivation and schooling in the middle grades. Review of Educational Research, 64(2), 287–309. https://doi.org/10.3102/00346543064002287
3.
AngristJ. D.HullP. D.PathakP. A.WaltersC. R. (2017). Leveraging lotteries for school value-added: Testing and estimation. Quarterly Journal of Economics, 132(2), 871–919. https://doi.org/10.1093/qje/qjx001
4.
AngristJ. D.LangK. (2004). Does school integration generate peer effects? Evidence from Boston’s Metco program. American Economic Review, 94(5), 1613–1634. https://doi.org/10.1257/0002828043052169
5.
BakerE. L.BartonP. E.Darling-HammondL.HaertelE.LaddH. F.LinnR. L.RavitchD.RothsteinR.ShavelsonR. J.ShepardL. A. (2010). Problems with the use of student test scores to evaluate teachers (Briefing Paper No. 278). Economic Policy Institute. https://files.epi.org/page/-/pdf/bp278.pdf
6.
BallouD.SandersW.WrightP. (2004). Controlling for student background in value-added assessment of teachers. Journal of Educational and Behavioral Statistics, 29(1), 37–65. https://doi.org/10.3102/10769986029001037
7.
BarrR.DreebenR. (1983). How schools work. University of Chicago Press.
8.
BerendsM.GoldringE.SteinM.CravensX. (2010). Instructional conditions in charter schools and students’ mathematics achievement gains. American Journal of Education, 116(3), 303–335. https://doi.org/10.1086/651411
9.
BriggsD. C.WeeksJ. P. (2011). The persistence of school-level value-added. Journal of Educational and Behavioral Statistics, 36(5), 616–637. https://doi.org/10.3102/1076998610396887
10.
ChettyR.FriedmanJ. N.HilgerN.SaezE.SchanzenbachD. W.YaganD. (2011). How does your kindergarten classroom affect your earnings? Evidence from Project Star. Quarterly Journal of Economics, 126(4), 1593–1660. https://doi.org/10.1093/qje/qjr041
11.
ChettyR.FriedmanJ. N.RockoffJ. E. (2014a). Measuring the impacts of teachers I: Evaluating bias in teacher value-added estimates. American Economic Review, 104(9), 2593–2632. https://doi.org/10.1257/aer.104.9.2593
12.
ChettyR.FriedmanJ. N.RockoffJ. E. (2014b). Measuring the impacts of teachers II: Teacher value-added and student outcomes in adulthood. American Economic Review, 104(9), 2633–2679. https://doi.org/10.1257/aer.104.9.2633
13.
ChettyR.FriedmanJ. N.RockoffJ. (2016). Using lagged outcomes to evaluate bias in value-added models. American Economic Review, 106(5), 393–399. https://doi.org/10.1257/aer.p20161081
14.
ChettyR.HendrenN.KatzL. F. (2016). The effects of exposure to better neighborhoods on children: New evidence from the moving to opportunity experiment. American Economic Review, 106(4), 855–902. https://doi.org/10.1257/aer.20150572
15.
CreemersB. P. M.ReezigtG. J. (1996). School level conditions affecting the effectiveness of instruction. School Effectiveness and School Improvement, 7, 197–229. https://doi.org/10.1080/0924345960070301
DemingD. J. (2009). Early childhood intervention and life-cycle skill development: Evidence from Head Start. American Economic Journal, 1(3), 111–134. https://doi.org/10.1257/app.1.3.111
18.
DemingD. J. (2014). Using school choice lotteries to test measures of school effectiveness. American Economic Review, 104(5), 406–411. https://doi.org/10.1257/aer.104.5.406
19.
DeutschJ. (2013). Using school lotteries to evaluate the value-added model. Society for Research on Educational Effectiveness.
20.
EversonK. C. (2017). Value-added modeling and educational accountability: Are we answering the real questions?Review of Educational Research, 87(1), 35–70. https://doi.org/10.3102/0034654316637199
GoddardY.GoddardR.KimM. (2015). School instructional climate and student achievement: An examination of group norms for differentiated instruction. American Journal of Education, 122(1), 111–131. https://doi.org/10.1086/683293
HeckmanJ. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science, 312(5782), 1900–1902. https://doi.org/10.1126/science.1128898
HeckmanJ. J.PintoR.SavelyevP. (2013). Understanding the mechanisms through which an influential early childhood program boosted adult outcomes. American Economic Review, 103(6), 2052–2086. https://doi.org/10.1257/aer.103.6.2052
28.
HeckmanJ. J.StixrudJ.UrzuaS. (2006). The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. Journal of Labor Economics, 24(3), 411–482. https://doi.org/10.1086/504455
29.
IsenbergE.HockH. (2012). Measuring school and teacher value added in DC, 2011-2012 school year. Mathematic Policy Research.
30.
JencksC.MayerS. E. (1990). The social consequences of growing up in a poor neighborhood. In LynnL.McGearyM. G. H. (Eds.), Inner-city poverty in the United States (pp. 111–186). National Academies Press.
31.
JenningsJ. L.DemingD.JencksC.LopuchM.SchuelerB. E. (2015). Do differences in school quality matter more than we thought? New evidence on educational opportunity in the twenty-first century. Sociology of Education, 88(1), 56–82. https://doi.org/10.1177/0038040714562006
32.
KaneT. J.StaigerD. O. (2001, August13). Rigid rules will damage schools. The New York Times, A21.
33.
KaneT. J.StaigerD. O. (2002). The promise and pitfalls of using imprecise school accountability measures. Journal of Economic Perspectives, 16(4), 91–114. https://doi.org/10.1257/089533002320950993
34.
KaneT. J.StaigerD. O. (2008). Estimating teacher impacts on student achievement: An experimental evaluation (Working Paper No. 14607). National Bureau of Economic Research. https://doi.org/10.3386/w14607
KonstantopoulosS. (2006). Trends of school effects on student achievement: Evidence from NLS: 72, HSB: 82, and NELS: 92. Teachers College Record, 108(12), 2550–2581.
37.
KonstantopoulosS. (2007a). A comment on variance decomposition and nesting effects in two- and three-level designs (IZA DP No. 3178). IZA—Institute of Labor Economics. http://ftp.iza.org/dp3178.pdf
KyriakidesL.CreemersB.AntoniouP.DemetriouD. (2010). A synthesis of studies searching for school factors: Implications for theory and research. British Educational Research Journal, 36(5), 807–830. https://doi.org/10.1080/01411920903165603
41.
LockwoodJ. R.McCaffreyD. F.MarianoL. T.SetodjiC. (2007). Bayesian methods for scalable multivariate value-added assessment. Journal of Educational and Behavioral Statistics, 32(2), 125–150. https://doi.org/10.3102/1076998606298039
LubienskiS. T.LubienskiC.CraneC. C. (2008). Achievement differences and school type: The role of school climate, teacher certification, and instruction. American Journal of Education, 115(1), 97–138. https://doi.org/10.1086/590677
44.
McCaffreyD. F.LockwoodJ. R.KoretzD. M.HamiltonL. S. (2008). The intertemporal stability of teacher effect estimates. National Center on Performance Incentives.
45.
McCaffreyD. F.LockwoodJ. R.KoretzD. M.LouisT. A.HamiltonL. (2004). Models for value-added modeling of teacher effects. Journal of Educational and Behavioral Statistics, 29(1), 67–102. https://doi.org/10.3102/10769986029001067
46.
NyeB.SpyrosK.HedgesL. V. (2004). How large are teacher effects?Educational Evaluation and Policy Analysis, 26(3), 237–257. https://doi.org/10.3102/01623737026003237
47.
OwensA. (2016). Inequality in children’s contexts: Income segregation of households with and without children. American Sociological Review, 81(3), 549–574. https://doi.org/10.1177/0003122416642430
48.
OwensA.ReardonS. F.JencksC. (2016). Income segregation between schools and school districts. American Educational Research Journal, 53(4), 1159–1197. https://doi.org/10.3102/0002831216652722
49.
RathundeK.CsikszentmihalyiM. (2005). Middle school students’ motivation and quality of experience: A comparison of Montessori and traditional school environments. American Journal of Education, 111(3), 341–371. https://doi.org/10.1086/428885
RaudenbushS. W.BrykA. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Sage.
52.
ReardonS.RaudenbushS. W. (2009). Assumptions of value-added models for estimating school effects. Educational Finance and Policy, 4(4), 492–519. https://doi.org/10.1162/edfp.2009.4.4.492
53.
RothsteinJ. (2009). Student sorting and bias in value-added estimation: Selection on observables and unobservables. Education Finance and Policy, 4(4), 537–571. https://doi.org/10.1162/edfp.2009.4.4.537
54.
RothsteinJ. (2010). Teacher quality in educational production: Tracking, decay, and student achievement. Quarterly Journal of Economics, 125(1), 175–214. https://doi.org/10.1162/qjec.2010.125.1.175
55.
SimmonsR. G.BlythD. A. (1987). Moving into adolescence: The impact of pubertal change and school context. Aldine.
56.
StaigerD. O.GeppertJ.KaneT. J. (2002). Randomly accountable. Education Next, 2(1), 57–61.
57.
von HippelP. T.BellowsL. (2018). How much does teacher quality vary across teacher preparation programs? Reanalyses from six states. Economics of Education Review, 64, 298–312. https://doi.org/10.1016/j.econedurev.2018.01.005
WigfieldA.JacquelynneE.MacIverD.ReumanD. A.MidgleyC. (1991). Transitions during early adolescence: Changes in children’s domain-specific self-perceptions and general self-esteem across the transition to junior high school. Developmental Psychology, 27(4), 552–565. https://doi.org/10.1037/0012-1649.27.4.552
60.
XuZ.SwanlundA. (2013). Estimating teacher contributions to student learning: The role of the school. American Institutes of Research.
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